1.1: all_pixels_compare_data_zero is a table of gpp, lai, et, gsdsr, apar and cica extracted from every pixel across the amazon and 5 regions over monthly time scale between 2001 and 2019

##       gpp              lai               cica             gsdsr              apar        
##  Min.   : 0.000   Min.   :0.07593   Min.   :0.05499   Min.   :0.02047   Min.   : 0.2157  
##  1st Qu.: 5.819   1st Qu.:2.40496   1st Qu.:0.74130   1st Qu.:0.02832   1st Qu.: 5.2664  
##  Median : 8.854   Median :4.61883   Median :0.77583   Median :0.03551   Median : 6.9921  
##  Mean   : 8.087   Mean   :4.03754   Mean   :0.74186   Mean   :0.21638   Mean   : 6.3363  
##  3rd Qu.:10.603   3rd Qu.:5.74994   3rd Qu.:0.79912   3rd Qu.:0.26842   3rd Qu.: 7.7616  
##  Max.   :16.258   Max.   :6.40442   Max.   :0.87102   Max.   :1.00000   Max.   :10.4986  
##                                                                                          
##        et           region_name          date               month               year          
##  Min.   :0.000   amazon_nw: 42408   Min.   :2001-01-01   Length:378252      Length:378252     
##  1st Qu.:2.249   amazon_sw: 34656   1st Qu.:2005-09-23   Class :character   Class :character  
##  Median :3.776   amazon_ec: 25536   Median :2010-06-16   Mode  :character   Mode  :character  
##  Mean   :3.266   amazon_bs: 98040   Mean   :2010-06-16                                        
##  3rd Qu.:4.304   amazon_gs: 31920   3rd Qu.:2015-03-08                                        
##  Max.   :6.388   amazonia :145692   Max.   :2019-12-01                                        
##                                                                                               
##     month_f      
##  Jan    : 31521  
##  Feb    : 31521  
##  Mar    : 31521  
##  Apr    : 31521  
##  May    : 31521  
##  Jun    : 31521  
##  (Other):189126
## 'data.frame':    378252 obs. of  11 variables:
##  $ gpp        : num  7.89 6.31 5.47 5.22 6.6 ...
##  $ lai        : num  2.92 2.49 2.21 2.05 2.78 ...
##  $ cica       : num  0.806 0.766 0.66 0.647 0.632 ...
##  $ gsdsr      : num  0.246 1 1 1 1 ...
##  $ apar       : num  6.41 6.25 5.86 5.34 6.47 ...
##  $ et         : num  1.97 1.45 1.03 1.12 1.62 ...
##  $ region_name: Factor w/ 6 levels "amazon_nw","amazon_sw",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2001-01-01" "2001-02-01" "2001-03-01" ...
##  $ month      : chr  "Jan" "Feb" "Mar" "Apr" ...
##  $ year       : chr  "2001" "2001" "2001" "2001" ...
##  $ month_f    : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 2 3 4 5 6 7 8 9 10 ...
##  - attr(*, "na.action")= 'omit' Named int [1:787512] 1 2 3 4 5 6 7 8 9 10 ...
##   ..- attr(*, "names")= chr [1:787512] "1" "2" "3" "4" ...
## 'data.frame':    1368 obs. of  8 variables:
##  $ region_name: Factor w/ 6 levels "amazon_nw","amazon_sw",..: 1 2 3 4 5 6 1 2 3 4 ...
##  $ new_date   : Factor w/ 228 levels "2001-01-01","2001-02-01",..: 1 1 1 1 1 1 2 2 2 2 ...
##  $ gpp        : num  8.31 6.62 10.07 6.52 10.19 ...
##  $ lai        : num  3.83 3.32 5.3 2.95 5.15 ...
##  $ cica       : num  0.759 0.743 0.742 0.746 0.747 ...
##  $ gsdsr      : num  0.222 0.338 0.036 0.382 0.144 ...
##  $ apar       : num  6.35 5.66 7.14 5.57 7.73 ...
##  $ et         : num  2.83 2.33 3.57 2.24 3.69 ...
## [1] "Monthly Mean"
## 'data.frame':    72 obs. of  8 variables:
##  $ region_name: Factor w/ 6 levels "amazon_nw","amazon_sw",..: 1 2 3 4 5 6 1 2 3 4 ...
##  $ month_f    : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 1 1 1 1 1 2 2 2 2 ...
##  $ gpp        : num  8.46 6.93 10.25 6.93 9.94 ...
##  $ lai        : num  3.85 3.32 5.24 2.9 5.07 ...
##  $ cica       : num  0.753 0.757 0.762 0.774 0.734 ...
##  $ gsdsr      : num  0.1637 0.2779 0.0355 0.3126 0.1709 ...
##  $ apar       : num  6.16 5.74 7.14 5.54 7.42 ...
##  $ et         : num  3.15 2.64 3.81 3.11 3.77 ...

1.2: Time series plot of the spatial mean variables across the amazon and 5 regions over monthly time scale between 2001 and 2019

1.3: Standardize data 1.4: Standardize all data 2.1: First, lm plots of various variable combinations for every region over the time period

2.2: Second, lm plots of various variable combinations for every region over the time period faceted monthly

2.3: lm plots of various variable combinations for every region over the time period representing months and region

2.4: lm plots of various monthly mean variable combinations for every region

2.5: lm plots of various monthly mean variable combinations for every region on scaled date 3.1: Third, hexbin plots of various variable combinations for every region over the time period

3.2: Fourth, hexbin plots of various variable combinations for every region over the time period